“Integrating Iris and Fingerprint Traits for Personal Authentication using Artificial Neural Network”

In recent years, biometric based security systems chieved more attention due to continuous terrorism threats around the world. However, a security system comprised of a single form of biometric information cannot fulfil user’s expectations and may suffer from noisy sensor data, intra and inter class variations and continuous spoof attacks. To overcome some of these problems, multimodal biometric aims at increasing the reliability of biometric systems through utilizing more than one biometric in decision-making process. In order to take full advantage of the multimodal approaches, an effective fusion scheme is necessary for combining information from various sources. I present a new methodology based on fusion at the feature level, which is a relatively new approach compared to others, to combine multimodal biometric information from two biometric identifiers (Iris and Fingerprint).The proposed system is for multimodal database comprising of 21 samples. The performance of the system is tested on a database prepared to find accuracy, false acceptance rate and false rejection rate